Learning Polymorphic Neural ODEs With Time-Evolving Mixture
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2022.3145013
Abstract: Neural ordinary differential equations (NODE) present a new way of considering a deep residual network as a continuous structure by layer depth. However, it fails to overcome its representational limits, where it cannot learn all… read more here.
Keywords: neural odes; ode; evolving mixture; time evolving ... See more keywords
Robust Prototypical Few-Shot Organ Segmentation with Regularized Neural-ODEs
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on medical imaging"
DOI: 10.48550/arxiv.2208.12428
Abstract: Despite the tremendous progress made by deep learning models in image semantic segmentation, they typically require large annotated examples, and increasing attention is being diverted to problem settings like Few-Shot Learning (FSL) where only a… read more here.
Keywords: neural odes; segmentation; organ segmentation; robust prototypical ... See more keywords